620 research outputs found
Elementary processes governing the evolution of road networks
Urbanisation is a fundamental phenomenon whose quantitative characterisation
is still inadequate. We report here the empirical analysis of a unique data set
regarding almost 200 years of evolution of the road network in a large area
located north of Milan (Italy). We find that urbanisation is characterised by
the homogenisation of cell shapes, and by the stability throughout time of
high-centrality roads which constitute the backbone of the urban structure,
confirming the importance of historical paths. We show quantitatively that the
growth of the network is governed by two elementary processes: (i)
`densification', corresponding to an increase in the local density of roads
around existing urban centres and (ii) `exploration', whereby new roads trigger
the spatial evolution of the urbanisation front. The empirical identification
of such simple elementary mechanisms suggests the existence of general, simple
properties of urbanisation and opens new directions for its modelling and
quantitative description.Comment: 10 pages, 6 figure
Traffic exhaust to wildfires: PM2.5 measurements with fixed and portable, low-cost LoRaWAN-connected sensors
© 2020 Forehead et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Air pollution with PM2.5 (particulate matter smaller than 2.5 micro-metres in diameter) is a major health hazard in many cities worldwide, but since measuring instruments have traditionally been expensive, monitoring sites are rare and generally show only background concentrations. With the advent of low-cost, wirelessly connected sensors, air quality measurements are increasingly being made in places where many people spend time and pollution is much worse: on streets near traffic. In the interests of enabling members of the public to measure the air that they breathe, we took an open-source approach to designing a device for measuring PM2.5. Parts are relatively cheap, but of good quality and can be easily found in electronics or hardware stores, or on-line. Software is open source and the free LoRaWAN-based “The Things Network” the platform. A number of low-cost sensors we tested had problems, but those selected performed well when co-located with reference-quality instruments. A network of the devices was deployed in an urban centre, yielding valuable data for an extended time. Concentrations of PM2.5 at street level were often ten times worse than at air quality stations. The devices and network offer the opportunity for measurements in locations that concern the public
Nutritional input from dinoflagellate symbionts in reef-building corals is minimal during planula larval life stage
Dispersion of larval offspring is of fundamental ecological importance to sessile marine organisms. Photosymbiotic planulae emitted by many reef-forming corals may travel over large distances before settling to form a new colony. It is not clear whether the metabolic requirements of these planula larvae are met exclusively with lipid and protein reservoirs inherited from the mother colony or when metabolic inputs from their endosymbiotic dinoflagellates become important. Pulse-chase experiments using [C-13] bicarbonate and [N-15] nitrate, combined with subcellular structural and isotopic imaging of freshly emitted symbiotic larvae from the coral Pocillopora damicornis, show that metabolic input from the dinoflagellates is minimal in the planulae compared with adult colonies. The larvae are essentially lecithotrophic upon emission, indicating that a marked shift in metabolic interaction between the symbiotic partners takes place later during ontogeny. Understanding the cellular processes that trigger and control this metabolic shift, and how climate change might influence it, is a key challenge in coral biology
Gait characterization in golden retriever muscular dystrophy dogs using linear discriminant analysis
The extreme vulnerability of interdependent spatially embedded networks
Recent studies show that in interdependent networks a very small failure in
one network may lead to catastrophic consequences. Above a critical fraction of
interdependent nodes, even a single node failure can invoke cascading failures
that may abruptly fragment the system, while below this "critical dependency"
(CD) a failure of few nodes leads only to small damage to the system. So far,
the research has been focused on interdependent random networks without space
limitations. However, many real systems, such as power grids and the Internet,
are not random but are spatially embedded. Here we analytically and numerically
analyze the stability of systems consisting of interdependent spatially
embedded networks modeled as lattice networks. Surprisingly, we find that in
lattice systems, in contrast to non-embedded systems, there is no CD and
\textit{any} small fraction of interdependent nodes leads to an abrupt
collapse. We show that this extreme vulnerability of very weakly coupled
lattices is a consequence of the critical exponent describing the percolation
transition of a single lattice. Our results are important for understanding the
vulnerabilities and for designing robust interdependent spatial embedded
networks.Comment: 13 pages, 5 figure
Resistance distance, information centrality, node vulnerability and vibrations in complex networks
We discuss three seemingly unrelated quantities that have been introduced in different fields of science for complex networks. The three quantities are the resistance distance, the information centrality and the node displacement. We first prove various relations among them. Then we focus on the node displacement, showing its usefulness as an index of node vulnerability.We argue that the node displacement has a better resolution as a measure of node vulnerability than the degree and the information centrality
Increased entropy of signal transduction in the cancer metastasis phenotype
Studies into the statistical properties of biological networks have led to
important biological insights, such as the presence of hubs and hierarchical
modularity. There is also a growing interest in studying the statistical
properties of networks in the context of cancer genomics. However, relatively
little is known as to what network features differ between the cancer and
normal cell physiologies, or between different cancer cell phenotypes. Based on
the observation that frequent genomic alterations underlie a more aggressive
cancer phenotype, we asked if such an effect could be detectable as an increase
in the randomness of local gene expression patterns. Using a breast cancer gene
expression data set and a model network of protein interactions we derive
constrained weighted networks defined by a stochastic information flux matrix
reflecting expression correlations between interacting proteins. Based on this
stochastic matrix we propose and compute an entropy measure that quantifies the
degree of randomness in the local pattern of information flux around single
genes. By comparing the local entropies in the non-metastatic versus metastatic
breast cancer networks, we here show that breast cancers that metastasize are
characterised by a small yet significant increase in the degree of randomness
of local expression patterns. We validate this result in three additional
breast cancer expression data sets and demonstrate that local entropy better
characterises the metastatic phenotype than other non-entropy based measures.
We show that increases in entropy can be used to identify genes and signalling
pathways implicated in breast cancer metastasis. Further exploration of such
integrated cancer expression and protein interaction networks will therefore be
a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table
Multiferroic phase transition near room temperature in BiFeO3 films
In multiferroic BiFeO3 thin films grown on highly mismatched LaAlO3
substrates, we reveal the coexistence of two differently distorted polymorphs
that leads to striking features in the temperature dependence of the structural
and multiferroic properties. Notably, the highly distorted phase
quasi-concomitantly presents an abrupt structural change, transforms from a
hard to a soft ferroelectric and transitions from antiferromagnetic to
paramagnetic at 360+/-20 K. These coupled ferroic transitions just above room
temperature hold promises of giant piezoelectric, magnetoelectric and
piezomagnetic responses, with potential in many applications fields
The Interspersed Spin Boson Lattice Model
We describe a family of lattice models that support a new class of quantum
magnetism characterized by correlated spin and bosonic ordering [Phys. Rev.
Lett. 112, 180405 (2014)]. We explore the full phase diagram of the model using
Matrix-Product-State methods. Guided by these numerical results, we describe a
modified variational ansatz to improve our analytic description of the
groundstate at low boson frequencies. Additionally, we introduce an
experimental protocol capable of inferring the low-energy excitations of the
system by means of Fano scattering spectroscopy. Finally, we discuss the
implementation and characterization of this model with current circuit-QED
technology.Comment: Submitted to EPJ ST issue on "Novel Quantum Phases and Mesoscopic
Physics in Quantum Gases
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